The demo works.
Now it has to work every day
Many companies already have a chatbot or a ChatGPT prototype that impresses in a meeting and fails on real cases. We add what production needs: tests on your data, monitoring, error handling, human review and logs.
Six layers between a demo and a system you can rely on
We keep what already works in your prototype and build the missing layers around it.
Evaluation set
Real cases from your data with correct answers, including the hard ones. Accuracy is reported per task, with false alarms and refusals.
Regression checks
The set runs on every change of prompt, model or code. A change that makes results worse does not reach users.
Error handling
Timeouts, rate limits, empty answers and broken output formats are caught, retried or sent to a person instead of failing silently.
Human review
Uncertain answers go to a queue with the source next to them. Corrections are logged and feed back into the evaluation set.
Monitoring
Volume, latency, cost, review share and correction rate on a dashboard, with alerts when something drifts.
Logs and access
Who asked what, which model version answered and who approved it, with role-based access and retention you configure.
From 1500 PLN (about €350)
That covers a review of the prototype, an evaluation set and the first production layers. The final price depends on the number of tasks and integrations and is fixed after a short look at your prototype.
What we need to see and what you get
We need
Access to the prototype or its code, 20–50 real examples it should handle, and a person who can say whether an answer is right.
You get
A report on where the prototype fails today, the evaluation set, the production layers in your repository and documentation for your team.
Related
Article: evals and human review→
How to check that an AI agent is not wrong.
AI agents for document review→
If your prototype reads documents.
Article: AI Act and GDPR→
Which EU rules apply to AI automation and when.
Forward-deployed engineering→
When there is no prototype yet and the process needs mapping first.
About taking AI to production
Have a prototype that never made it to real users?
Tell us what it does, what it runs on and where it fails. We reply within one business day.